pip install pandas matplotlib seaborn numpy
Requirement already satisfied: pandas in c:\users\cheta\anaconda3\lib\site-packages (2.0.3) Requirement already satisfied: matplotlib in c:\users\cheta\anaconda3\lib\site-packages (3.7.2) Requirement already satisfied: seaborn in c:\users\cheta\anaconda3\lib\site-packages (0.12.2) Requirement already satisfied: numpy in c:\users\cheta\anaconda3\lib\site-packages (1.24.3) Requirement already satisfied: python-dateutil>=2.8.2 in c:\users\cheta\anaconda3\lib\site-packages (from pandas) (2.8.2) Requirement already satisfied: pytz>=2020.1 in c:\users\cheta\anaconda3\lib\site-packages (from pandas) (2023.3.post1) Requirement already satisfied: tzdata>=2022.1 in c:\users\cheta\anaconda3\lib\site-packages (from pandas) (2023.3) Requirement already satisfied: contourpy>=1.0.1 in c:\users\cheta\anaconda3\lib\site-packages (from matplotlib) (1.0.5) Requirement already satisfied: cycler>=0.10 in c:\users\cheta\anaconda3\lib\site-packages (from matplotlib) (0.11.0) Requirement already satisfied: fonttools>=4.22.0 in c:\users\cheta\anaconda3\lib\site-packages (from matplotlib) (4.25.0) Requirement already satisfied: kiwisolver>=1.0.1 in c:\users\cheta\anaconda3\lib\site-packages (from matplotlib) (1.4.4) Requirement already satisfied: packaging>=20.0 in c:\users\cheta\anaconda3\lib\site-packages (from matplotlib) (23.1) Requirement already satisfied: pillow>=6.2.0 in c:\users\cheta\anaconda3\lib\site-packages (from matplotlib) (9.4.0) Requirement already satisfied: pyparsing<3.1,>=2.3.1 in c:\users\cheta\anaconda3\lib\site-packages (from matplotlib) (3.0.9) Requirement already satisfied: six>=1.5 in c:\users\cheta\anaconda3\lib\site-packages (from python-dateutil>=2.8.2->pandas) (1.16.0) Note: you may need to restart the kernel to use updated packages.
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
gd = pd.read_csv('GSDP.csv')
gd = pd.read_csv('GSDP.csv')
# Display first few rows to understand the structure of the data
gd.head(), gd.head()
( Items Description Duration Andhra Pradesh \
0 GSDP - CURRENT PRICES (` in Crore) 2011-12 379402.0
1 GSDP - CURRENT PRICES (` in Crore) 2012-13 411404.0
2 GSDP - CURRENT PRICES (` in Crore) 2013-14 464272.0
3 GSDP - CURRENT PRICES (` in Crore) 2014-15 526468.0
4 GSDP - CURRENT PRICES (` in Crore) 2015-16 609934.0
Arunachal Pradesh Assam Bihar Chhattisgarh Goa Gujarat \
0 11063.0 143175.0 247144.0 158074.0 42367.0 615606.0
1 12547.0 156864.0 282368.0 177511.0 38120.0 724495.0
2 14602.0 177745.0 317101.0 206690.0 35921.0 807623.0
3 16761.0 198098.0 373920.0 234982.0 40633.0 895027.0
4 18784.0 224234.0 413503.0 260776.0 45002.0 994316.0
Haryana ... Telangana Tripura Uttar Pradesh Uttarakhand \
0 297539.0 ... 359433.0 19208.0 724049.0 115523.0
1 347032.0 ... 401493.0 21663.0 822903.0 131835.0
2 400662.0 ... 452186.0 25593.0 944146.0 149817.0
3 437462.0 ... 511178.0 29667.0 1043371.0 161985.0
4 485184.0 ... 575631.0 NaN 1153795.0 184091.0
West Bengal1 Andaman & Nicobar Islands Chandigarh Delhi Puducherry \
0 NaN 3979.0 18768.0 343767.0 16818.0
1 NaN 4421.0 21609.0 391238.0 18875.0
2 NaN 5159.0 24787.0 443783.0 21870.0
3 NaN 5721.0 27844.0 492424.0 24089.0
4 NaN NaN 30304.0 551963.0 26533.0
All_India GDP
0 8736039.0
1 9946636.0
2 11236635.0
3 12433749.0
4 13675331.0
[5 rows x 36 columns],
Items Description Duration Andhra Pradesh \
0 GSDP - CURRENT PRICES (` in Crore) 2011-12 379402.0
1 GSDP - CURRENT PRICES (` in Crore) 2012-13 411404.0
2 GSDP - CURRENT PRICES (` in Crore) 2013-14 464272.0
3 GSDP - CURRENT PRICES (` in Crore) 2014-15 526468.0
4 GSDP - CURRENT PRICES (` in Crore) 2015-16 609934.0
Arunachal Pradesh Assam Bihar Chhattisgarh Goa Gujarat \
0 11063.0 143175.0 247144.0 158074.0 42367.0 615606.0
1 12547.0 156864.0 282368.0 177511.0 38120.0 724495.0
2 14602.0 177745.0 317101.0 206690.0 35921.0 807623.0
3 16761.0 198098.0 373920.0 234982.0 40633.0 895027.0
4 18784.0 224234.0 413503.0 260776.0 45002.0 994316.0
Haryana ... Telangana Tripura Uttar Pradesh Uttarakhand \
0 297539.0 ... 359433.0 19208.0 724049.0 115523.0
1 347032.0 ... 401493.0 21663.0 822903.0 131835.0
2 400662.0 ... 452186.0 25593.0 944146.0 149817.0
3 437462.0 ... 511178.0 29667.0 1043371.0 161985.0
4 485184.0 ... 575631.0 NaN 1153795.0 184091.0
West Bengal1 Andaman & Nicobar Islands Chandigarh Delhi Puducherry \
0 NaN 3979.0 18768.0 343767.0 16818.0
1 NaN 4421.0 21609.0 391238.0 18875.0
2 NaN 5159.0 24787.0 443783.0 21870.0
3 NaN 5721.0 27844.0 492424.0 24089.0
4 NaN NaN 30304.0 551963.0 26533.0
All_India GDP
0 8736039.0
1 9946636.0
2 11236635.0
3 12433749.0
4 13675331.0
[5 rows x 36 columns])
gd = pd.read_csv('GSDP.csv')
gd.head()
| Items Description | Duration | Andhra Pradesh | Arunachal Pradesh | Assam | Bihar | Chhattisgarh | Goa | Gujarat | Haryana | ... | Telangana | Tripura | Uttar Pradesh | Uttarakhand | West Bengal1 | Andaman & Nicobar Islands | Chandigarh | Delhi | Puducherry | All_India GDP | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | GSDP - CURRENT PRICES (` in Crore) | 2011-12 | 379402.0 | 11063.0 | 143175.0 | 247144.0 | 158074.0 | 42367.0 | 615606.0 | 297539.0 | ... | 359433.0 | 19208.0 | 724049.0 | 115523.0 | NaN | 3979.0 | 18768.0 | 343767.0 | 16818.0 | 8736039.0 |
| 1 | GSDP - CURRENT PRICES (` in Crore) | 2012-13 | 411404.0 | 12547.0 | 156864.0 | 282368.0 | 177511.0 | 38120.0 | 724495.0 | 347032.0 | ... | 401493.0 | 21663.0 | 822903.0 | 131835.0 | NaN | 4421.0 | 21609.0 | 391238.0 | 18875.0 | 9946636.0 |
| 2 | GSDP - CURRENT PRICES (` in Crore) | 2013-14 | 464272.0 | 14602.0 | 177745.0 | 317101.0 | 206690.0 | 35921.0 | 807623.0 | 400662.0 | ... | 452186.0 | 25593.0 | 944146.0 | 149817.0 | NaN | 5159.0 | 24787.0 | 443783.0 | 21870.0 | 11236635.0 |
| 3 | GSDP - CURRENT PRICES (` in Crore) | 2014-15 | 526468.0 | 16761.0 | 198098.0 | 373920.0 | 234982.0 | 40633.0 | 895027.0 | 437462.0 | ... | 511178.0 | 29667.0 | 1043371.0 | 161985.0 | NaN | 5721.0 | 27844.0 | 492424.0 | 24089.0 | 12433749.0 |
| 4 | GSDP - CURRENT PRICES (` in Crore) | 2015-16 | 609934.0 | 18784.0 | 224234.0 | 413503.0 | 260776.0 | 45002.0 | 994316.0 | 485184.0 | ... | 575631.0 | NaN | 1153795.0 | 184091.0 | NaN | NaN | 30304.0 | 551963.0 | 26533.0 | 13675331.0 |
5 rows × 36 columns
gd.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 11 entries, 0 to 10 Data columns (total 36 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Items Description 11 non-null object 1 Duration 11 non-null object 2 Andhra Pradesh 11 non-null float64 3 Arunachal Pradesh 9 non-null float64 4 Assam 9 non-null float64 5 Bihar 9 non-null float64 6 Chhattisgarh 11 non-null float64 7 Goa 9 non-null float64 8 Gujarat 9 non-null float64 9 Haryana 11 non-null float64 10 Himachal Pradesh 7 non-null float64 11 Jammu & Kashmir 9 non-null float64 12 Jharkhand 9 non-null float64 13 Karnataka 9 non-null float64 14 Kerala 9 non-null float64 15 Madhya Pradesh 11 non-null float64 16 Maharashtra 7 non-null float64 17 Manipur 7 non-null float64 18 Meghalaya 11 non-null float64 19 Mizoram 7 non-null float64 20 Nagaland 7 non-null float64 21 Odisha 11 non-null float64 22 Punjab 7 non-null float64 23 Rajasthan 7 non-null float64 24 Sikkim 9 non-null float64 25 Tamil Nadu 11 non-null float64 26 Telangana 11 non-null float64 27 Tripura 7 non-null float64 28 Uttar Pradesh 9 non-null float64 29 Uttarakhand 9 non-null float64 30 West Bengal1 0 non-null float64 31 Andaman & Nicobar Islands 7 non-null float64 32 Chandigarh 9 non-null float64 33 Delhi 11 non-null float64 34 Puducherry 11 non-null float64 35 All_India GDP 11 non-null float64 dtypes: float64(34), object(2) memory usage: 3.2+ KB
gd.columns
Index(['Items Description', 'Duration', 'Andhra Pradesh ',
'Arunachal Pradesh', 'Assam', 'Bihar', 'Chhattisgarh', 'Goa', 'Gujarat',
'Haryana', 'Himachal Pradesh', 'Jammu & Kashmir', 'Jharkhand',
'Karnataka', 'Kerala', 'Madhya Pradesh', 'Maharashtra', 'Manipur',
'Meghalaya', 'Mizoram', 'Nagaland', 'Odisha', 'Punjab', 'Rajasthan',
'Sikkim', 'Tamil Nadu', 'Telangana', 'Tripura', 'Uttar Pradesh',
'Uttarakhand', 'West Bengal1', 'Andaman & Nicobar Islands',
'Chandigarh', 'Delhi', 'Puducherry', 'All_India GDP'],
dtype='object')
gd1 = gd[gd['Duration'] != '2016-17']
gd1
| Items Description | Duration | Andhra Pradesh | Arunachal Pradesh | Assam | Bihar | Chhattisgarh | Goa | Gujarat | Haryana | ... | Telangana | Tripura | Uttar Pradesh | Uttarakhand | West Bengal1 | Andaman & Nicobar Islands | Chandigarh | Delhi | Puducherry | All_India GDP | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | GSDP - CURRENT PRICES (` in Crore) | 2011-12 | 379402.00 | 11063.00 | 143175.00 | 247144.00 | 158074.00 | 42367.00 | 615606.00 | 297539.00 | ... | 359433.00 | 19208.00 | 724049.00 | 115523.00 | NaN | 3979.00 | 18768.00 | 343767.00 | 16818.00 | 8736039.00 |
| 1 | GSDP - CURRENT PRICES (` in Crore) | 2012-13 | 411404.00 | 12547.00 | 156864.00 | 282368.00 | 177511.00 | 38120.00 | 724495.00 | 347032.00 | ... | 401493.00 | 21663.00 | 822903.00 | 131835.00 | NaN | 4421.00 | 21609.00 | 391238.00 | 18875.00 | 9946636.00 |
| 2 | GSDP - CURRENT PRICES (` in Crore) | 2013-14 | 464272.00 | 14602.00 | 177745.00 | 317101.00 | 206690.00 | 35921.00 | 807623.00 | 400662.00 | ... | 452186.00 | 25593.00 | 944146.00 | 149817.00 | NaN | 5159.00 | 24787.00 | 443783.00 | 21870.00 | 11236635.00 |
| 3 | GSDP - CURRENT PRICES (` in Crore) | 2014-15 | 526468.00 | 16761.00 | 198098.00 | 373920.00 | 234982.00 | 40633.00 | 895027.00 | 437462.00 | ... | 511178.00 | 29667.00 | 1043371.00 | 161985.00 | NaN | 5721.00 | 27844.00 | 492424.00 | 24089.00 | 12433749.00 |
| 4 | GSDP - CURRENT PRICES (` in Crore) | 2015-16 | 609934.00 | 18784.00 | 224234.00 | 413503.00 | 260776.00 | 45002.00 | 994316.00 | 485184.00 | ... | 575631.00 | NaN | 1153795.00 | 184091.00 | NaN | NaN | 30304.00 | 551963.00 | 26533.00 | 13675331.00 |
| 6 | (% Growth over previous year) | 2012-13 | 8.43 | 13.41 | 9.56 | 14.25 | 12.30 | -10.02 | 17.69 | 16.63 | ... | 11.70 | 12.78 | 13.65 | 14.12 | NaN | 11.13 | 15.14 | 13.81 | 12.23 | 13.86 |
| 7 | (% Growth over previous year) | 2013-14 | 12.85 | 16.38 | 13.31 | 12.30 | 16.44 | -5.77 | 11.47 | 15.45 | ... | 12.63 | 18.14 | 14.73 | 13.64 | NaN | 16.68 | 14.71 | 13.43 | 15.87 | 12.97 |
| 8 | (% Growth over previous year) | 2014-15 | 13.40 | 14.79 | 11.45 | 17.92 | 13.69 | 13.12 | 10.82 | 9.18 | ... | 13.05 | 15.92 | 10.51 | 8.12 | NaN | 10.89 | 12.33 | 10.96 | 10.14 | 10.65 |
| 9 | (% Growth over previous year) | 2015-16 | 15.85 | 12.07 | 13.19 | 10.59 | 10.98 | 10.75 | 11.09 | 10.91 | ... | 12.61 | NaN | 10.58 | 13.65 | NaN | NaN | 8.84 | 12.09 | 10.15 | 9.99 |
9 rows × 36 columns
gd1.isnull().sum()
Items Description 0 Duration 0 Andhra Pradesh 0 Arunachal Pradesh 0 Assam 0 Bihar 0 Chhattisgarh 0 Goa 0 Gujarat 0 Haryana 0 Himachal Pradesh 2 Jammu & Kashmir 0 Jharkhand 0 Karnataka 0 Kerala 0 Madhya Pradesh 0 Maharashtra 2 Manipur 2 Meghalaya 0 Mizoram 2 Nagaland 2 Odisha 0 Punjab 2 Rajasthan 2 Sikkim 0 Tamil Nadu 0 Telangana 0 Tripura 2 Uttar Pradesh 0 Uttarakhand 0 West Bengal1 9 Andaman & Nicobar Islands 2 Chandigarh 0 Delhi 0 Puducherry 0 All_India GDP 0 dtype: int64
gd1.isnull().all(axis=0)
Items Description False Duration False Andhra Pradesh False Arunachal Pradesh False Assam False Bihar False Chhattisgarh False Goa False Gujarat False Haryana False Himachal Pradesh False Jammu & Kashmir False Jharkhand False Karnataka False Kerala False Madhya Pradesh False Maharashtra False Manipur False Meghalaya False Mizoram False Nagaland False Odisha False Punjab False Rajasthan False Sikkim False Tamil Nadu False Telangana False Tripura False Uttar Pradesh False Uttarakhand False West Bengal1 True Andaman & Nicobar Islands False Chandigarh False Delhi False Puducherry False All_India GDP False dtype: bool
gd1.shape
(9, 36)
gd1.iloc[6:].isnull().sum()
Items Description 0 Duration 0 Andhra Pradesh 0 Arunachal Pradesh 0 Assam 0 Bihar 0 Chhattisgarh 0 Goa 0 Gujarat 0 Haryana 0 Himachal Pradesh 1 Jammu & Kashmir 0 Jharkhand 0 Karnataka 0 Kerala 0 Madhya Pradesh 0 Maharashtra 1 Manipur 1 Meghalaya 0 Mizoram 1 Nagaland 1 Odisha 0 Punjab 1 Rajasthan 1 Sikkim 0 Tamil Nadu 0 Telangana 0 Tripura 1 Uttar Pradesh 0 Uttarakhand 0 West Bengal1 3 Andaman & Nicobar Islands 1 Chandigarh 0 Delhi 0 Puducherry 0 All_India GDP 0 dtype: int64
avg_growth = gd1.iloc[6:]
avg_growth
| Items Description | Duration | Andhra Pradesh | Arunachal Pradesh | Assam | Bihar | Chhattisgarh | Goa | Gujarat | Haryana | ... | Telangana | Tripura | Uttar Pradesh | Uttarakhand | West Bengal1 | Andaman & Nicobar Islands | Chandigarh | Delhi | Puducherry | All_India GDP | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 7 | (% Growth over previous year) | 2013-14 | 12.85 | 16.38 | 13.31 | 12.30 | 16.44 | -5.77 | 11.47 | 15.45 | ... | 12.63 | 18.14 | 14.73 | 13.64 | NaN | 16.68 | 14.71 | 13.43 | 15.87 | 12.97 |
| 8 | (% Growth over previous year) | 2014-15 | 13.40 | 14.79 | 11.45 | 17.92 | 13.69 | 13.12 | 10.82 | 9.18 | ... | 13.05 | 15.92 | 10.51 | 8.12 | NaN | 10.89 | 12.33 | 10.96 | 10.14 | 10.65 |
| 9 | (% Growth over previous year) | 2015-16 | 15.85 | 12.07 | 13.19 | 10.59 | 10.98 | 10.75 | 11.09 | 10.91 | ... | 12.61 | NaN | 10.58 | 13.65 | NaN | NaN | 8.84 | 12.09 | 10.15 | 9.99 |
3 rows × 36 columns
avg_growth.columns
Index(['Items Description', 'Duration', 'Andhra Pradesh ',
'Arunachal Pradesh', 'Assam', 'Bihar', 'Chhattisgarh', 'Goa', 'Gujarat',
'Haryana', 'Himachal Pradesh', 'Jammu & Kashmir', 'Jharkhand',
'Karnataka', 'Kerala', 'Madhya Pradesh', 'Maharashtra', 'Manipur',
'Meghalaya', 'Mizoram', 'Nagaland', 'Odisha', 'Punjab', 'Rajasthan',
'Sikkim', 'Tamil Nadu', 'Telangana', 'Tripura', 'Uttar Pradesh',
'Uttarakhand', 'West Bengal1', 'Andaman & Nicobar Islands',
'Chandigarh', 'Delhi', 'Puducherry', 'All_India GDP'],
dtype='object')
average_growth_values = avg_growth[avg_growth.columns[2:34]].mean()
average_growth_values = average_growth_values.sort_values()
average_growth_rate = average_growth_values.to_frame(name='Average growth rate')
average_growth_rate
| Average growth rate | |
|---|---|
| Goa | 6.033333 |
| Meghalaya | 6.953333 |
| Odisha | 9.836667 |
| Sikkim | 10.486667 |
| Jammu & Kashmir | 10.900000 |
| Gujarat | 11.126667 |
| Punjab | 11.185000 |
| Maharashtra | 11.260000 |
| Rajasthan | 11.320000 |
| Jharkhand | 11.500000 |
| Uttarakhand | 11.803333 |
| Haryana | 11.846667 |
| Uttar Pradesh | 11.940000 |
| Chandigarh | 11.960000 |
| Delhi | 12.160000 |
| Himachal Pradesh | 12.280000 |
| Tamil Nadu | 12.336667 |
| Kerala | 12.583333 |
| Madhya Pradesh | 12.626667 |
| Assam | 12.650000 |
| Telangana | 12.763333 |
| Bihar | 13.603333 |
| Chhattisgarh | 13.703333 |
| Andaman & Nicobar Islands | 13.785000 |
| Andhra Pradesh | 14.033333 |
| Karnataka | 14.120000 |
| Arunachal Pradesh | 14.413333 |
| Manipur | 14.610000 |
| Nagaland | 16.415000 |
| Tripura | 17.030000 |
| Mizoram | 17.700000 |
| West Bengal1 | NaN |
plt.figure(figsize=(12,10), dpi = 300)
sns.barplot(x = average_growth_rate['Average growth rate'], y = average_growth_values.index,palette='viridis')
plt.xlabel('Average Growth Rate', fontsize=12)
plt.ylabel('States', fontsize=12)
plt.title('Average Growth Rate for all the states',fontsize=13)
plt.show()
average_growth_rate['Average growth rate'][-5:]
Manipur 14.610 Nagaland 16.415 Tripura 17.030 Mizoram 17.700 West Bengal1 NaN Name: Average growth rate, dtype: float64
avg_growth[['Mizoram','Tripura','Nagaland','Manipur','Arunachal Pradesh']]
| Mizoram | Tripura | Nagaland | Manipur | Arunachal Pradesh | |
|---|---|---|---|---|---|
| 7 | 23.1 | 18.14 | 21.98 | 17.83 | 16.38 |
| 8 | 12.3 | 15.92 | 10.85 | 11.39 | 14.79 |
| 9 | NaN | NaN | NaN | NaN | 12.07 |
describe = pd.DataFrame(avg_growth.describe())
describe = describe.T
describe
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| Andhra Pradesh | 3.0 | 14.033333 | 1.597133 | 12.85 | 13.1250 | 13.400 | 14.6250 | 15.85 |
| Arunachal Pradesh | 3.0 | 14.413333 | 2.179549 | 12.07 | 13.4300 | 14.790 | 15.5850 | 16.38 |
| Assam | 3.0 | 12.650000 | 1.040961 | 11.45 | 12.3200 | 13.190 | 13.2500 | 13.31 |
| Bihar | 3.0 | 13.603333 | 3.834871 | 10.59 | 11.4450 | 12.300 | 15.1100 | 17.92 |
| Chhattisgarh | 3.0 | 13.703333 | 2.730024 | 10.98 | 12.3350 | 13.690 | 15.0650 | 16.44 |
| Goa | 3.0 | 6.033333 | 10.290444 | -5.77 | 2.4900 | 10.750 | 11.9350 | 13.12 |
| Gujarat | 3.0 | 11.126667 | 0.326548 | 10.82 | 10.9550 | 11.090 | 11.2800 | 11.47 |
| Haryana | 3.0 | 11.846667 | 3.238245 | 9.18 | 10.0450 | 10.910 | 13.1800 | 15.45 |
| Himachal Pradesh | 2.0 | 12.280000 | 3.026417 | 10.14 | 11.2100 | 12.280 | 13.3500 | 14.42 |
| Jammu & Kashmir | 3.0 | 10.900000 | 6.642146 | 4.70 | 7.3950 | 10.090 | 14.0000 | 17.91 |
| Jharkhand | 3.0 | 11.500000 | 3.610374 | 7.92 | 9.6800 | 11.440 | 13.2900 | 15.14 |
| Karnataka | 3.0 | 14.120000 | 3.624969 | 11.42 | 12.0600 | 12.700 | 15.4700 | 18.24 |
| Kerala | 3.0 | 12.583333 | 0.654930 | 11.85 | 12.3200 | 12.790 | 12.9500 | 13.11 |
| Madhya Pradesh | 3.0 | 12.626667 | 2.408492 | 10.11 | 11.4850 | 12.860 | 13.8850 | 14.91 |
| Maharashtra | 2.0 | 11.260000 | 3.507250 | 8.78 | 10.0200 | 11.260 | 12.5000 | 13.74 |
| Manipur | 2.0 | 14.610000 | 4.553768 | 11.39 | 13.0000 | 14.610 | 16.2200 | 17.83 |
| Meghalaya | 3.0 | 6.953333 | 2.401548 | 4.87 | 5.6400 | 6.410 | 7.9950 | 9.58 |
| Mizoram | 2.0 | 17.700000 | 7.636753 | 12.30 | 15.0000 | 17.700 | 20.4000 | 23.10 |
| Nagaland | 2.0 | 16.415000 | 7.870098 | 10.85 | 13.6325 | 16.415 | 19.1975 | 21.98 |
| Odisha | 3.0 | 9.836667 | 3.411412 | 6.19 | 8.2800 | 10.370 | 11.6600 | 12.95 |
| Punjab | 2.0 | 11.185000 | 1.746554 | 9.95 | 10.5675 | 11.185 | 11.8025 | 12.42 |
| Rajasthan | 2.0 | 11.320000 | 0.070711 | 11.27 | 11.2950 | 11.320 | 11.3450 | 11.37 |
| Sikkim | 3.0 | 10.486667 | 1.622108 | 9.39 | 9.5550 | 9.720 | 11.0350 | 12.35 |
| Tamil Nadu | 3.0 | 12.336667 | 1.268910 | 10.99 | 11.7500 | 12.510 | 13.0100 | 13.51 |
| Telangana | 3.0 | 12.763333 | 0.248462 | 12.61 | 12.6200 | 12.630 | 12.8400 | 13.05 |
| Tripura | 2.0 | 17.030000 | 1.569777 | 15.92 | 16.4750 | 17.030 | 17.5850 | 18.14 |
| Uttar Pradesh | 3.0 | 11.940000 | 2.416464 | 10.51 | 10.5450 | 10.580 | 12.6550 | 14.73 |
| Uttarakhand | 3.0 | 11.803333 | 3.189864 | 8.12 | 10.8800 | 13.640 | 13.6450 | 13.65 |
| West Bengal1 | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| Andaman & Nicobar Islands | 2.0 | 13.785000 | 4.094148 | 10.89 | 12.3375 | 13.785 | 15.2325 | 16.68 |
| Chandigarh | 3.0 | 11.960000 | 2.952440 | 8.84 | 10.5850 | 12.330 | 13.5200 | 14.71 |
| Delhi | 3.0 | 12.160000 | 1.236487 | 10.96 | 11.5250 | 12.090 | 12.7600 | 13.43 |
| Puducherry | 3.0 | 12.053333 | 3.305334 | 10.14 | 10.1450 | 10.150 | 13.0100 | 15.87 |
| All_India GDP | 3.0 | 11.203333 | 1.565162 | 9.99 | 10.3200 | 10.650 | 11.8100 | 12.97 |
describe[(describe['mean']>12) & (describe['std']<2)]
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| Andhra Pradesh | 3.0 | 14.033333 | 1.597133 | 12.85 | 13.125 | 13.40 | 14.625 | 15.85 |
| Assam | 3.0 | 12.650000 | 1.040961 | 11.45 | 12.320 | 13.19 | 13.250 | 13.31 |
| Kerala | 3.0 | 12.583333 | 0.654930 | 11.85 | 12.320 | 12.79 | 12.950 | 13.11 |
| Tamil Nadu | 3.0 | 12.336667 | 1.268910 | 10.99 | 11.750 | 12.51 | 13.010 | 13.51 |
| Telangana | 3.0 | 12.763333 | 0.248462 | 12.61 | 12.620 | 12.63 | 12.840 | 13.05 |
| Tripura | 2.0 | 17.030000 | 1.569777 | 15.92 | 16.475 | 17.03 | 17.585 | 18.14 |
| Delhi | 3.0 | 12.160000 | 1.236487 | 10.96 | 11.525 | 12.09 | 12.760 | 13.43 |
describe[(describe['mean']<12) & (describe['std']>2)]
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| Goa | 3.0 | 6.033333 | 10.290444 | -5.77 | 2.490 | 10.75 | 11.935 | 13.12 |
| Haryana | 3.0 | 11.846667 | 3.238245 | 9.18 | 10.045 | 10.91 | 13.180 | 15.45 |
| Jammu & Kashmir | 3.0 | 10.900000 | 6.642146 | 4.70 | 7.395 | 10.09 | 14.000 | 17.91 |
| Jharkhand | 3.0 | 11.500000 | 3.610374 | 7.92 | 9.680 | 11.44 | 13.290 | 15.14 |
| Maharashtra | 2.0 | 11.260000 | 3.507250 | 8.78 | 10.020 | 11.26 | 12.500 | 13.74 |
| Meghalaya | 3.0 | 6.953333 | 2.401548 | 4.87 | 5.640 | 6.41 | 7.995 | 9.58 |
| Odisha | 3.0 | 9.836667 | 3.411412 | 6.19 | 8.280 | 10.37 | 11.660 | 12.95 |
| Uttar Pradesh | 3.0 | 11.940000 | 2.416464 | 10.51 | 10.545 | 10.58 | 12.655 | 14.73 |
| Uttarakhand | 3.0 | 11.803333 | 3.189864 | 8.12 | 10.880 | 13.64 | 13.645 | 13.65 |
| Chandigarh | 3.0 | 11.960000 | 2.952440 | 8.84 | 10.585 | 12.33 | 13.520 | 14.71 |
gd1 = gd[gd['Duration'] != '2015-16']
gd1.head()
| Items Description | Duration | Andhra Pradesh | Arunachal Pradesh | Assam | Bihar | Chhattisgarh | Goa | Gujarat | Haryana | ... | Telangana | Tripura | Uttar Pradesh | Uttarakhand | West Bengal1 | Andaman & Nicobar Islands | Chandigarh | Delhi | Puducherry | All_India GDP | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | GSDP - CURRENT PRICES (` in Crore) | 2011-12 | 379402.0 | 11063.0 | 143175.0 | 247144.0 | 158074.0 | 42367.0 | 615606.0 | 297539.0 | ... | 359433.0 | 19208.0 | 724049.0 | 115523.0 | NaN | 3979.0 | 18768.0 | 343767.0 | 16818.0 | 8736039.0 |
| 1 | GSDP - CURRENT PRICES (` in Crore) | 2012-13 | 411404.0 | 12547.0 | 156864.0 | 282368.0 | 177511.0 | 38120.0 | 724495.0 | 347032.0 | ... | 401493.0 | 21663.0 | 822903.0 | 131835.0 | NaN | 4421.0 | 21609.0 | 391238.0 | 18875.0 | 9946636.0 |
| 2 | GSDP - CURRENT PRICES (` in Crore) | 2013-14 | 464272.0 | 14602.0 | 177745.0 | 317101.0 | 206690.0 | 35921.0 | 807623.0 | 400662.0 | ... | 452186.0 | 25593.0 | 944146.0 | 149817.0 | NaN | 5159.0 | 24787.0 | 443783.0 | 21870.0 | 11236635.0 |
| 3 | GSDP - CURRENT PRICES (` in Crore) | 2014-15 | 526468.0 | 16761.0 | 198098.0 | 373920.0 | 234982.0 | 40633.0 | 895027.0 | 437462.0 | ... | 511178.0 | 29667.0 | 1043371.0 | 161985.0 | NaN | 5721.0 | 27844.0 | 492424.0 | 24089.0 | 12433749.0 |
| 5 | GSDP - CURRENT PRICES (` in Crore) | 2016-17 | 699307.0 | NaN | NaN | NaN | 290140.0 | NaN | NaN | 547396.0 | ... | 654294.0 | NaN | NaN | NaN | NaN | NaN | NaN | 622385.0 | 29557.0 | 15251028.0 |
5 rows × 36 columns
total_GDP_15_16 = gd1[(gd1['Items Description'] == 'GSDP - CURRENT PRICES (` in Crore)')]
total_GDP_15_16
| Items Description | Duration | Andhra Pradesh | Arunachal Pradesh | Assam | Bihar | Chhattisgarh | Goa | Gujarat | Haryana | ... | Telangana | Tripura | Uttar Pradesh | Uttarakhand | West Bengal1 | Andaman & Nicobar Islands | Chandigarh | Delhi | Puducherry | All_India GDP | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | GSDP - CURRENT PRICES (` in Crore) | 2011-12 | 379402.0 | 11063.0 | 143175.0 | 247144.0 | 158074.0 | 42367.0 | 615606.0 | 297539.0 | ... | 359433.0 | 19208.0 | 724049.0 | 115523.0 | NaN | 3979.0 | 18768.0 | 343767.0 | 16818.0 | 8736039.0 |
| 1 | GSDP - CURRENT PRICES (` in Crore) | 2012-13 | 411404.0 | 12547.0 | 156864.0 | 282368.0 | 177511.0 | 38120.0 | 724495.0 | 347032.0 | ... | 401493.0 | 21663.0 | 822903.0 | 131835.0 | NaN | 4421.0 | 21609.0 | 391238.0 | 18875.0 | 9946636.0 |
| 2 | GSDP - CURRENT PRICES (` in Crore) | 2013-14 | 464272.0 | 14602.0 | 177745.0 | 317101.0 | 206690.0 | 35921.0 | 807623.0 | 400662.0 | ... | 452186.0 | 25593.0 | 944146.0 | 149817.0 | NaN | 5159.0 | 24787.0 | 443783.0 | 21870.0 | 11236635.0 |
| 3 | GSDP - CURRENT PRICES (` in Crore) | 2014-15 | 526468.0 | 16761.0 | 198098.0 | 373920.0 | 234982.0 | 40633.0 | 895027.0 | 437462.0 | ... | 511178.0 | 29667.0 | 1043371.0 | 161985.0 | NaN | 5721.0 | 27844.0 | 492424.0 | 24089.0 | 12433749.0 |
| 5 | GSDP - CURRENT PRICES (` in Crore) | 2016-17 | 699307.0 | NaN | NaN | NaN | 290140.0 | NaN | NaN | 547396.0 | ... | 654294.0 | NaN | NaN | NaN | NaN | NaN | NaN | 622385.0 | 29557.0 | 15251028.0 |
5 rows × 36 columns
total_GDP_15_16_states = total_GDP_15_16[total_GDP_15_16.columns[2:34]].transpose()
total_GDP_15_16_states = total_GDP_15_16_states.rename(columns={4: 'Total GDP of States 2015-16'})
total_GDP_15_16_states = total_GDP_15_16_states.dropna()
total_GDP_15_16_states
| 0 | 1 | 2 | 3 | 5 | |
|---|---|---|---|---|---|
| Andhra Pradesh | 379402.0 | 411404.0 | 464272.0 | 526468.0 | 699307.0 |
| Chhattisgarh | 158074.0 | 177511.0 | 206690.0 | 234982.0 | 290140.0 |
| Haryana | 297539.0 | 347032.0 | 400662.0 | 437462.0 | 547396.0 |
| Madhya Pradesh | 315561.0 | 380924.0 | 437737.0 | 481982.0 | 640484.0 |
| Meghalaya | 19918.0 | 21872.0 | 22938.0 | 24408.0 | 29567.0 |
| Odisha | 227872.0 | 258275.0 | 291709.0 | 321971.0 | 378991.0 |
| Tamil Nadu | 751485.0 | 855481.0 | 971090.0 | 1092564.0 | 1338766.0 |
| Telangana | 359433.0 | 401493.0 | 452186.0 | 511178.0 | 654294.0 |
| Delhi | 343767.0 | 391238.0 | 443783.0 | 492424.0 | 622385.0 |
top_5_eco = total_GDP_15_16_states[-5:]
top_5_eco
| 0 | 1 | 2 | 3 | 5 | |
|---|---|---|---|---|---|
| Meghalaya | 19918.0 | 21872.0 | 22938.0 | 24408.0 | 29567.0 |
| Odisha | 227872.0 | 258275.0 | 291709.0 | 321971.0 | 378991.0 |
| Tamil Nadu | 751485.0 | 855481.0 | 971090.0 | 1092564.0 | 1338766.0 |
| Telangana | 359433.0 | 401493.0 | 452186.0 | 511178.0 | 654294.0 |
| Delhi | 343767.0 | 391238.0 | 443783.0 | 492424.0 | 622385.0 |
bottom_5_eco = total_GDP_15_16_states[:5]
bottom_5_eco
| 0 | 1 | 2 | 3 | 5 | |
|---|---|---|---|---|---|
| Andhra Pradesh | 379402.0 | 411404.0 | 464272.0 | 526468.0 | 699307.0 |
| Chhattisgarh | 158074.0 | 177511.0 | 206690.0 | 234982.0 | 290140.0 |
| Haryana | 297539.0 | 347032.0 | 400662.0 | 437462.0 | 547396.0 |
| Madhya Pradesh | 315561.0 | 380924.0 | 437737.0 | 481982.0 | 640484.0 |
| Meghalaya | 19918.0 | 21872.0 | 22938.0 | 24408.0 | 29567.0 |
Andhra_Pradesh = pd.read_csv('NAD-Andhra_Pradesh-GSVA_cur_2016-17.csv')
Arunachal_Pradesh = pd.read_csv('NAD-Arunachal_Pradesh-GSVA_cur_2015-16.csv')
Assam = pd.read_csv('NAD-Assam-GSVA_cur_2015-16.csv')
Bihar = pd.read_csv('NAD-Bihar-GSVA_cur_2015-16.csv')
Chhattisgarh = pd.read_csv('NAD-Chhattisgarh-GSVA_cur_2016-17.csv')
Goa = pd.read_csv('NAD-Goa-GSVA_cur_2015-16.csv')
Gujarat = pd.read_csv('NAD-Gujarat-GSVA_cur_2015-16.csv')
Haryana = pd.read_csv('NAD-Haryana-GSVA_cur_2016-17.csv')
Himachal_Pradesh = pd.read_csv('NAD-Himachal_Pradesh-GSVA_cur_2014-15.csv')
Jharkhand = pd.read_csv('NAD-Jharkhand-GSVA_cur_2015-16.csv')
Karnataka = pd.read_csv('NAD-Karnataka-GSVA_cur_2015-16.csv')
Kerala = pd.read_csv('NAD-Kerala-GSVA_cur_2015-16.csv')
Madhya_Pradesh = pd.read_csv('NAD-Madhya_Pradesh-GSVA_cur_2016-17.csv')
Maharashtra = pd.read_csv('NAD-Maharashtra-GSVA_cur_2014-15.csv')
Meghalaya = pd.read_csv('NAD-Meghalaya-GSVA_cur_2016-17.csv')
Mizoram = pd.read_csv('NAD-Mizoram-GSVA_cur_2014-15.csv')
Nagaland = pd.read_csv('NAD-Nagaland-GSVA_cur_2014-15.csv')
Odisha = pd.read_csv('NAD-Odisha-GSVA_cur_2016-17.csv')
Punjab = pd.read_csv('NAD-Punjab-GSVA_cur_2014-15.csv')
Rajasthan = pd.read_csv('NAD-Rajasthan-GSVA_cur_2014-15.csv')
Sikkim = pd.read_csv('NAD-Sikkim-GSVA_cur_2015-16.csv')
Tamil_Nadu = pd.read_csv('NAD-Tamil_Nadu-GSVA_cur_2016-17.csv')
Telangana = pd.read_csv('NAD-Telangana-GSVA_cur_2016-17.csv')
Tripura = pd.read_csv('NAD-Tripura-GSVA_cur_2014-15.csv')
Uttar_Pradesh = pd.read_csv('NAD-Uttar_Pradesh-GSVA_cur_2015-16.csv')
Uttarakhand = pd.read_csv('NAD-Uttarakhand-GSVA_cur_2015-16.csv')
andhra_pradesh = Andhra_Pradesh[['S.No.','Item', '2014-15']]
andhra_pradesh = andhra_pradesh.rename(columns={'2014-15': 'Andhra_Pradesh'})
arunachal_pradesh = Arunachal_Pradesh[['S.No.','Item', '2014-15']]
arunachal_pradesh = arunachal_pradesh.rename(columns={'2014-15': 'Arunachal_Pradesh'})
assam = Assam[['S.No.','Item', '2014-15']]
assam = assam.rename(columns={'2014-15': 'Assam'})
bihar = Bihar[['S.No.','Item', '2014-15']]
bihar = bihar.rename(columns={'2014-15': 'Bihar'})
chhattisgarh = Chhattisgarh[['S.No.','Item', '2014-15']]
chhattisgarh = chhattisgarh.rename(columns={'2014-15': 'Chhattisgarh'})
goa = Goa[['S.No.','Item', '2014-15']]
goa = goa.rename(columns={'2014-15': 'Goa'})
gujarat = Gujarat[['S.No.','Item', '2014-15']]
gujarat = gujarat.rename(columns={'2014-15': 'Gujarat'})
haryana = Haryana[['S.No.','Item', '2014-15']]
haryana = haryana.rename(columns={'2014-15': 'Haryana'})
himachal_Pradesh = Himachal_Pradesh[['S.No.','Item', '2014-15']]
himachal_Pradesh = himachal_Pradesh.rename(columns={'2014-15': 'Himachal_Pradesh'})
jharkhand = Jharkhand[['S.No.','Item', '2014-15']]
jharkhand = jharkhand.rename(columns={'2014-15': 'Jharkhand'})
karnataka = Karnataka[['S.No.','Item', '2014-15']]
karnataka = karnataka.rename(columns={'2014-15': 'Karnataka'})
kerala = Kerala[['S.No.','Item', '2014-15']]
kerala = kerala.rename(columns={'2014-15': 'Kerala'})
madhya_pradesh = Madhya_Pradesh[['S.No.','Item', '2014-15']]
madhya_pradesh = madhya_pradesh.rename(columns={'2014-15': 'Madhya_Pradesh'})
maharashtra = Maharashtra[['S.No.','Item', '2014-15']]
maharashtra = maharashtra.rename(columns={'2014-15': 'Maharashtra'})
meghalaya = Meghalaya[['S.No.','Item', '2014-15']]
meghalaya = meghalaya.rename(columns={'2014-15': 'Meghalaya'})
mizoram = Mizoram[['S.No.','Item', '2014-15']]
mizoram = mizoram.rename(columns={'2014-15': 'Mizoram'})
nagaland = Nagaland[['S.No.','Item', '2014-15']]
nagaland = nagaland.rename(columns={'2014-15': 'Nagaland'})
odisha = Odisha[['S.No.','Item', '2014-15']]
odisha = odisha.rename(columns={'2014-15': 'Odisha'})
punjab = Punjab[['S.No.','Item', '2014-15']]
punjab = punjab.rename(columns={'2014-15': 'Punjab'})
rajasthan = Rajasthan[['S.No.','Item', '2014-15']]
rajasthan = rajasthan.rename(columns={'2014-15': 'Rajasthan'})
sikkim = Sikkim[['S.No.','Item', '2014-15']]
sikkim = sikkim.rename(columns={'2014-15': 'Sikkim'})
tamil_nadu = Tamil_Nadu[['S.No.','Item', '2014-15']]
tamil_nadu = tamil_nadu.rename(columns={'2014-15': 'Tamil_Nadu'})
telangana = Telangana[['S.No.','Item', '2014-15']]
telangana = telangana.rename(columns={'2014-15': 'Telangana'})
tripura = Tripura[['S.No.','Item', '2014-15']]
tripura = tripura.rename(columns={'2014-15': 'Tripura'})
uttar_pradesh = Uttar_Pradesh[['S.No.','Item', '2014-15']]
uttar_pradesh = uttar_pradesh.rename(columns={'2014-15': 'Uttar_Pradesh'})
uttarakhand = Uttarakhand[['S.No.','Item', '2014-15']]
uttarakhand = uttarakhand.rename(columns={'2014-15': 'Uttarakhand'})
dfs = [andhra_pradesh,arunachal_pradesh, assam, bihar, chhattisgarh, goa, gujarat, haryana,himachal_Pradesh,
jharkhand, karnataka,kerala,madhya_pradesh, maharashtra,meghalaya,mizoram, nagaland,odisha,
punjab,rajasthan,sikkim,tamil_nadu,telangana,tripura,uttarakhand, uttar_pradesh]
from functools import reduce
df_final = reduce(lambda left,right: pd.merge(left,right,how ='left',on=['S.No.', 'Item']), dfs)
df_final.columns
Index(['S.No.', 'Item', 'Andhra_Pradesh', 'Arunachal_Pradesh', 'Assam',
'Bihar', 'Chhattisgarh', 'Goa', 'Gujarat', 'Haryana',
'Himachal_Pradesh', 'Jharkhand', 'Karnataka', 'Kerala',
'Madhya_Pradesh', 'Maharashtra', 'Meghalaya', 'Mizoram', 'Nagaland',
'Odisha', 'Punjab', 'Rajasthan', 'Sikkim', 'Tamil_Nadu', 'Telangana',
'Tripura', 'Uttarakhand', 'Uttar_Pradesh'],
dtype='object')
df_final = df_final.rename(columns={'Andhra_Pradesh':'Andhra Pradesh', 'Arunachal_Pradesh':'Arunachal Pradesh',
'Himachal_Pradesh':'Himachal Pradesh','Madhya_Pradesh':'Madhya Pradesh',
'Tamil_Nadu':'Tamil Nadu','Uttar_Pradesh':'Uttar Pradesh',
'Chhattisgarh':'Chhatisgarh','Uttarakhand':'Uttrakhand'})
df_final
| S.No. | Item | Andhra Pradesh | Arunachal Pradesh | Assam | Bihar | Chhatisgarh | Goa | Gujarat | Haryana | ... | Nagaland | Odisha | Punjab | Rajasthan | Sikkim | Tamil Nadu | Telangana | Tripura | Uttrakhand | Uttar Pradesh | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | Agriculture, forestry and fishing | 14819416 | 686117 | 3855548 | 7951890 | 3948847 | 308507 | 13769969.00 | 8015238.0 | ... | 607897 | 6422978 | 9285716 | 15044394 | 137447 | 13064238.0 | 7591501 | 799825.0 | 1601423.0 | 25097754 |
| 1 | 1.1 | Crops | 7893514 | 415520 | 2890544 | 4688237 | 2613371 | 140421 | 9671086.00 | 4636731.0 | ... | 375825 | 4382636 | 5690972 | 7673441 | 114976 | 7297820.0 | 4162493 | 397591.0 | 866146.0 | 16215212 |
| 2 | 1.2 | Livestock | 4309078 | 38387 | 173478 | 2060296 | 352208 | 30141 | 2698910.00 | 2916173.0 | ... | 123800 | 788243 | 2638842 | 5356257 | 17338 | 4693361.0 | 2951299 | 88176.0 | 391188.0 | 7096876 |
| 3 | 1.3 | Forestry and logging | 346160 | 224017 | 261987 | 550132 | 597785 | 15744 | 761616.00 | 352254.0 | ... | 99802 | 791463 | 848245 | 1956660 | 4529 | 392705.0 | 210741 | 145096.0 | 339293.0 | 1404936 |
| 4 | 1.4 | Fishing and aquaculture | 2270664 | 8193 | 529539 | 653224 | 385483 | 122201 | 638357.00 | 110080.0 | ... | 8470 | 460636 | 107657 | 58036 | 604 | 680352.0 | 266968 | 168961.0 | 4796.0 | 380730 |
| 5 | 2 | Mining and quarrying | 1484300 | 30842 | 1471149 | 68107 | 2451970 | 3622 | 2117218.00 | 25186.0 | ... | 8280 | 2586328 | 10354 | 4069385 | 1329 | 265536.0 | 1541853 | 142391.0 | 244549.0 | 901501 |
| 6 | Total | Primary | 16303716 | 716959 | 5326697 | 8019997 | 6400817 | 312129 | 15887187.00 | 8040424.0 | ... | 616178 | 9009306 | 9296070 | 19113780 | 138776 | 13329774.0 | 9133354 | 942216.0 | 1845972.0 | 25999255 |
| 7 | 3 | Manufacturing | 4672266 | 26120 | 2002936 | 2189965 | 4370593 | 1177608 | 24087538.00 | 7756921.0 | ... | 18346 | 5754229 | 4790341 | 6552580 | 550697 | 18914794.0 | 6353711 | 228625.0 | 5866252.0 | 12261649 |
| 8 | 4 | Electricity, gas, water supply & other utility... | 1151729 | 113527 | 296587 | 345168 | 1198438 | 204110 | 3409983.00 | 1101919.0 | ... | 37944 | 833067 | 911611 | 1122888 | 212499 | 1710379.0 | 716266 | 77870.0 | 433880.0 | 2030625 |
| 9 | 5 | Construction | 4664889 | 147842 | 1733568 | 3449763 | 2669855 | 165819 | 5526017.00 | 3702571.0 | ... | 156072 | 2402396 | 2202962 | 5353326 | 82058 | 12216718.0 | 2854024 | 177899.0 | 1342733.0 | 11256450 |
| 10 | Total | Secondary | 10488884 | 287489 | 4033091 | 5984896 | 8238886 | 1547536 | 33023538.00 | 12561411.0 | ... | 212361 | 8989693 | 7904914 | 13028794 | 845253 | 32841892.0 | 9924001 | 484393.0 | 7642865.0 | 25548724 |
| 11 | 6 | Trade, repair, hotels and restaurants | 4233400 | 60421 | 2987155 | 7448373 | 1535571 | 380927 | 10178713.00 | 4986319.0 | ... | 140781 | 3149555 | 4419919 | 7297290 | 70568 | 12895842.0 | 6494607 | 390423.0 | 1743106.0 | 9437243 |
| 12 | 6.1 | Trade & repair services | 3716000 | 56796 | 2876251 | 7081391 | 1414164 | 343492 | 10178713.00 | 4817784.0 | ... | 134174 | 2886789 | 4201252 | 6942748 | 64624 | 11252588.0 | 5724128 | 390423.0 | 1534073.0 | 8476139 |
| 13 | 6.2 | Hotels & restaurants | 517400 | 3625 | 110904 | 366982 | 121407 | 37434 | NaN | 168535.0 | ... | 6607 | 262766 | 218667 | 354543 | 5945 | 1643253.0 | 770479 | NaN | 209033.0 | 961104 |
| 14 | 7 | Transport, storage, communication & services r... | 5076984 | 35203 | 1194568 | 3147173 | 871770 | 189656 | 4555910.00 | 2560623.0 | ... | 77521 | 2034016 | 1951809 | 3814461 | 47347 | 7188320.0 | 3604741 | 155956.0 | 1066693.0 | 7404509 |
| 15 | 7.1 | Railways | 424228 | 59 | 252509 | 462413 | 159176 | 15649 | 511593.00 | 423873.0 | ... | 336 | 341494 | 233389 | 464638 | 0 | 468553.0 | 199686 | 305.0 | 21295.0 | 1618742 |
| 16 | 7.2 | Road transport | 2816000 | 15467 | 507668 | 1572288 | 386628 | 46171 | NaN | 1452364.0 | ... | 34548 | 973144 | 928575 | 2121206 | 35283 | 3660994.0 | 2055658 | NaN | NaN | 3645747 |
| 17 | 7.3 | Water transport | 94200 | 0 | 4502 | 2228 | 0 | 17820 | NaN | NaN | ... | 600 | 50349 | 0 | 0 | 0 | 70414.0 | 0 | NaN | NaN | 681 |
| 18 | 7.4 | Air transport | 14900 | 0 | 26223 | 13599 | 9507 | 46359 | NaN | NaN | ... | 4153 | 15354 | 4473 | 13469 | 0 | 180836.0 | 120691 | NaN | 3889.0 | 36582 |
| 19 | 7.5 | Services incidental to transport | 780200 | 109 | 35739 | 166600 | 5232 | 19272 | NaN | 190269.0 | ... | 0 | 117469 | 48124 | 47609 | 0 | NaN | 454909 | NaN | -76.0 | 16323 |
| 20 | 7.6 | Storage | 18700 | 0 | 10308 | 10618 | 16675 | 357 | 57634.00 | 14459.0 | ... | 89 | 22675 | 76429 | 16584 | 0 | 39834.0 | 19805 | 254.0 | 660.0 | 171696 |
| 21 | 7.7 | Communication & services related to broadcasting | 928756 | 19568 | 357619 | 919427 | 294552 | 44028 | 1242520.00 | 479658.0 | ... | 37794 | 513531 | 660819 | 1150955 | 12064 | 1903283.0 | 753992 | 66676.0 | 733778.0 | 1914737 |
| 22 | 8 | Financial services | 1900863 | 25207 | 543651 | 1178022 | 739057 | 233618 | 4606644.00 | 1671486.0 | ... | 60393 | 1065147 | 2057520 | 1827413 | 21079 | 5598498.0 | 3023729 | 86094.0 | 385030.0 | 3392275 |
| 23 | 9 | Real estate, ownership of dwelling & professio... | 4405409 | 48418 | 1412466 | 3740641 | 2462166 | 407099 | 5179502.00 | 6970183.0 | ... | 159651 | 2348714 | 3142786 | 6451997 | 75330 | 16830213.0 | 9478839 | 190704.0 | 831307.0 | 14548185 |
| 24 | 10 | Public administration | 2200897 | 243867 | 1373611 | 2078171 | 867982 | 346486 | 2576195.00 | 1036377.0 | ... | 295424 | 1318221 | 1842730 | 2460364 | 119514 | 3400800.0 | 1711265 | 338244.0 | 579409.0 | 6152124 |
| 25 | 11 | Other services | 4215389 | 218728 | 1795658 | 4587589 | 1112232 | 180431 | 3123413.00 | 2001581.0 | ... | 259186 | 2340603 | 3303041 | 4164287 | 149265 | 7430115.0 | 4158229 | 323287.0 | 982430.0 | 5034623 |
| 26 | Total | Tertiary | 22032942 | 631844 | 9307109 | 22179969 | 7588778 | 1738217 | 30220377.00 | 19226568.0 | ... | 992956 | 12256258 | 16717805 | 26015812 | 483103 | 53343788.0 | 28471410 | 1484709.0 | 5587975.0 | 45968959 |
| 27 | 12 | TOTAL GSVA at basic prices | 48825542 | 1636292 | 18666897 | 36184863 | 22228481 | 3597882 | 79131102.00 | 39828404.0 | ... | 1821495 | 30255256 | 33918789 | 58158386 | 1467133 | 99515453.0 | 47528765 | 2911319.0 | 15076812.0 | 97516938 |
| 28 | 13 | Taxes on Products | 5512100 | 70099 | 1725309 | 3213546 | 2601791 | 527279 | 12353171.04 | 4985670.0 | ... | 57674 | 3151184 | 3794100 | 5394503 | 72200 | 12507325.0 | 4425700 | 149345.0 | 1434856.0 | 10107396 |
| 29 | 14 | Subsidies on products | 1690800 | 30272 | 582406 | 2006421 | 1332092 | 61854 | 1981546.00 | 1067867.0 | ... | 37745 | 1209349 | 911800 | 2333442 | 18400 | 2766405.0 | 836700 | 94002.0 | 313139.0 | 3287219 |
| 30 | 15 | Gross State Domestic Product | 52646842 | 1676119 | 19809800 | 37391988 | 23498180 | 4063307 | 89502727.00 | 43746207.0 | ... | 1841424 | 32197092 | 36801089 | 61219447 | 1520933 | 109256373.0 | 51117765 | 2966662.0 | 16198529.0 | 104337115 |
| 31 | 16 | Population ('00) | 501510 | 14870 | 326780 | 1101240 | 270530 | 14950 | 633590.00 | 266620.0 | ... | 20550 | 435220 | 290673 | 721610 | 6330 | 745760.0 | 367660 | 38350.0 | 105820.0 | 2109940 |
| 32 | 17 | Per Capita GSDP (Rs.) | 104977 | 112718 | 60621 | 33954 | 86860 | 271793 | 141263.00 | 164077.0 | ... | 89607 | 73979 | 126606 | 84837 | 240274 | 146503.0 | 139035 | 77358.0 | 153076.0 | 49450 |
33 rows × 28 columns
gdp_per_capita = df_final.iloc[32][2:].sort_values()
gdp_per_capita = gdp_per_capita.to_frame(name = 'GDP per capita')
gdp_per_capita
| GDP per capita | |
|---|---|
| Bihar | 33954 |
| Uttar Pradesh | 49450 |
| Assam | 60621 |
| Jharkhand | 62091 |
| Madhya Pradesh | 62989 |
| Odisha | 73979 |
| Meghalaya | 76228.0 |
| Tripura | 77358.0 |
| Rajasthan | 84837 |
| Chhatisgarh | 86860 |
| Nagaland | 89607 |
| Mizoram | 97687 |
| Andhra Pradesh | 104977 |
| Arunachal Pradesh | 112718 |
| Punjab | 126606 |
| Telangana | 139035 |
| Gujarat | 141263.0 |
| Karnataka | 145141 |
| Tamil Nadu | 146503.0 |
| Himachal Pradesh | 147330 |
| Maharashtra | 152853 |
| Uttrakhand | 153076.0 |
| Kerala | 154778.0 |
| Haryana | 164077.0 |
| Sikkim | 240274 |
| Goa | 271793 |
plt.figure(figsize=(12,8), dpi=600)
sns.barplot(x = gdp_per_capita['GDP per capita'], y =gdp_per_capita.index, palette='Reds' )
plt.xlabel('GDP per capita', fontsize=12)
plt.ylabel('States', fontsize=12)
plt.title('GDP per capita vs States',fontsize=12)
plt.show()
top_5_gdp_per_capita = gdp_per_capita[-5:]
top_5_gdp_per_capita
| GDP per capita | |
|---|---|
| Uttrakhand | 153076.0 |
| Kerala | 154778.0 |
| Haryana | 164077.0 |
| Sikkim | 240274 |
| Goa | 271793 |
bottom_5_gdp_per_capita = gdp_per_capita[:5]
bottom_5_gdp_per_capita
| GDP per capita | |
|---|---|
| Bihar | 33954 |
| Uttar Pradesh | 49450 |
| Assam | 60621 |
| Jharkhand | 62091 |
| Madhya Pradesh | 62989 |
ratio = gdp_per_capita['GDP per capita'].max()/gdp_per_capita['GDP per capita'].min()
print('The Ratio of highest per capita GDP to the lowest per capita GDP is: ',ratio)
The Ratio of highest per capita GDP to the lowest per capita GDP is: 8.004741709371503
primary = df_final[df_final['Item']=='Primary']
secondary = df_final[df_final['Item']=='Secondary']
tertiary = df_final[df_final['Item']=='Tertiary']
gdp = df_final[df_final['Item']=='Gross State Domestic Product']
pst = pd.concat([primary, secondary,tertiary,gdp], axis = 0).reset_index()
pst = pst.drop(['index','S.No.'], axis = 1).set_index('Item')
pst
| Andhra Pradesh | Arunachal Pradesh | Assam | Bihar | Chhatisgarh | Goa | Gujarat | Haryana | Himachal Pradesh | Jharkhand | ... | Nagaland | Odisha | Punjab | Rajasthan | Sikkim | Tamil Nadu | Telangana | Tripura | Uttrakhand | Uttar Pradesh | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Item | |||||||||||||||||||||
| Primary | 16303716 | 716959 | 5326697 | 8019997 | 6400817 | 312129 | 15887187.0 | 8040424.0 | 1548366 | 5248354 | ... | 616178 | 9009306 | 9296070 | 19113780 | 138776 | 13329774.0 | 9133354 | 942216.0 | 1845972.0 | 25999255 |
| Secondary | 10488884 | 287489 | 4033091 | 5984896 | 8238886 | 1547536 | 33023538.0 | 12561411.0 | 4119162 | 6241471 | ... | 212361 | 8989693 | 7904914 | 13028794 | 845253 | 32841892.0 | 9924001 | 484393.0 | 7642865.0 | 25548724 |
| Tertiary | 22032942 | 631844 | 9307109 | 22179969 | 7588778 | 1738217 | 30220377.0 | 19226568.0 | 4133326 | 8133341 | ... | 992956 | 12256258 | 16717805 | 26015812 | 483103 | 53343788.0 | 28471410 | 1484709.0 | 5587975.0 | 45968959 |
| Gross State Domestic Product | 52646842 | 1676119 | 19809800 | 37391988 | 23498180 | 4063307 | 89502727.0 | 43746207.0 | 10436879 | 21710718 | ... | 1841424 | 32197092 | 36801089 | 61219447 | 1520933 | 109256373.0 | 51117765 | 2966662.0 | 16198529.0 | 104337115 |
4 rows × 26 columns
pst.loc['primary_percentage'] = pst.loc['Primary'] / pst.loc['Gross State Domestic Product'] * 100
pst.loc['secondary_percentage'] = pst.loc['Secondary'] / pst.loc['Gross State Domestic Product'] * 100
pst.loc['tertiary_percentage'] = pst.loc['Tertiary'] / pst.loc['Gross State Domestic Product'] * 100
pst
| Andhra Pradesh | Arunachal Pradesh | Assam | Bihar | Chhatisgarh | Goa | Gujarat | Haryana | Himachal Pradesh | Jharkhand | ... | Nagaland | Odisha | Punjab | Rajasthan | Sikkim | Tamil Nadu | Telangana | Tripura | Uttrakhand | Uttar Pradesh | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Item | |||||||||||||||||||||
| Primary | 1.630372e+07 | 7.169590e+05 | 5.326697e+06 | 8.019997e+06 | 6.400817e+06 | 3.121290e+05 | 1.588719e+07 | 8.040424e+06 | 1.548366e+06 | 5.248354e+06 | ... | 6.161780e+05 | 9.009306e+06 | 9.296070e+06 | 1.911378e+07 | 1.387760e+05 | 1.332977e+07 | 9.133354e+06 | 9.422160e+05 | 1.845972e+06 | 2.599926e+07 |
| Secondary | 1.048888e+07 | 2.874890e+05 | 4.033091e+06 | 5.984896e+06 | 8.238886e+06 | 1.547536e+06 | 3.302354e+07 | 1.256141e+07 | 4.119162e+06 | 6.241471e+06 | ... | 2.123610e+05 | 8.989693e+06 | 7.904914e+06 | 1.302879e+07 | 8.452530e+05 | 3.284189e+07 | 9.924001e+06 | 4.843930e+05 | 7.642865e+06 | 2.554872e+07 |
| Tertiary | 2.203294e+07 | 6.318440e+05 | 9.307109e+06 | 2.217997e+07 | 7.588778e+06 | 1.738217e+06 | 3.022038e+07 | 1.922657e+07 | 4.133326e+06 | 8.133341e+06 | ... | 9.929560e+05 | 1.225626e+07 | 1.671780e+07 | 2.601581e+07 | 4.831030e+05 | 5.334379e+07 | 2.847141e+07 | 1.484709e+06 | 5.587975e+06 | 4.596896e+07 |
| Gross State Domestic Product | 5.264684e+07 | 1.676119e+06 | 1.980980e+07 | 3.739199e+07 | 2.349818e+07 | 4.063307e+06 | 8.950273e+07 | 4.374621e+07 | 1.043688e+07 | 2.171072e+07 | ... | 1.841424e+06 | 3.219709e+07 | 3.680109e+07 | 6.121945e+07 | 1.520933e+06 | 1.092564e+08 | 5.111776e+07 | 2.966662e+06 | 1.619853e+07 | 1.043371e+08 |
| primary_percentage | 3.096808e+01 | 4.277495e+01 | 2.688920e+01 | 2.144844e+01 | 2.723963e+01 | 7.681649e+00 | 1.775051e+01 | 1.837971e+01 | 1.483553e+01 | 2.417402e+01 | ... | 3.346204e+01 | 2.798174e+01 | 2.526031e+01 | 3.122175e+01 | 9.124399e+00 | 1.220045e+01 | 1.786728e+01 | 3.176014e+01 | 1.139592e+01 | 2.491851e+01 |
| secondary_percentage | 1.992310e+01 | 1.715206e+01 | 2.035907e+01 | 1.600582e+01 | 3.506180e+01 | 3.808563e+01 | 3.689668e+01 | 2.871429e+01 | 3.946737e+01 | 2.874834e+01 | ... | 1.153243e+01 | 2.792082e+01 | 2.148011e+01 | 2.128212e+01 | 5.557464e+01 | 3.005947e+01 | 1.941400e+01 | 1.632788e+01 | 4.718246e+01 | 2.448671e+01 |
| tertiary_percentage | 4.185045e+01 | 3.769685e+01 | 4.698235e+01 | 5.931744e+01 | 3.229517e+01 | 4.277838e+01 | 3.376476e+01 | 4.395025e+01 | 3.960308e+01 | 3.746233e+01 | ... | 5.392327e+01 | 3.806635e+01 | 4.542747e+01 | 4.249599e+01 | 3.176360e+01 | 4.882442e+01 | 5.569768e+01 | 5.004645e+01 | 3.449681e+01 | 4.405811e+01 |
7 rows × 26 columns
pst = pst.T
pst = pst.sort_values('Gross State Domestic Product')
pst
| Item | Primary | Secondary | Tertiary | Gross State Domestic Product | primary_percentage | secondary_percentage | tertiary_percentage |
|---|---|---|---|---|---|---|---|
| Mizoram | 225598.0 | 270072.0 | 637619.0 | 1155933.0 | 19.516529 | 23.363984 | 55.160550 |
| Sikkim | 138776.0 | 845253.0 | 483103.0 | 1520933.0 | 9.124399 | 55.574637 | 31.763595 |
| Arunachal Pradesh | 716959.0 | 287489.0 | 631844.0 | 1676119.0 | 42.774946 | 17.152064 | 37.696846 |
| Nagaland | 616178.0 | 212361.0 | 992956.0 | 1841424.0 | 33.462038 | 11.532434 | 53.923268 |
| Meghalaya | 451050.0 | 637942.0 | 1200655.0 | 2440807.0 | 18.479544 | 26.136520 | 49.190903 |
| Tripura | 942216.0 | 484393.0 | 1484709.0 | 2966662.0 | 31.760140 | 16.327880 | 50.046450 |
| Goa | 312129.0 | 1547536.0 | 1738217.0 | 4063307.0 | 7.681649 | 38.085628 | 42.778382 |
| Himachal Pradesh | 1548366.0 | 4119162.0 | 4133326.0 | 10436879.0 | 14.835527 | 39.467373 | 39.603084 |
| Uttrakhand | 1845972.0 | 7642865.0 | 5587975.0 | 16198529.0 | 11.395924 | 47.182463 | 34.496805 |
| Assam | 5326697.0 | 4033091.0 | 9307109.0 | 19809800.0 | 26.889201 | 20.359070 | 46.982347 |
| Jharkhand | 5248354.0 | 6241471.0 | 8133341.0 | 21710718.0 | 24.174023 | 28.748340 | 37.462331 |
| Chhatisgarh | 6400817.0 | 8238886.0 | 7588778.0 | 23498180.0 | 27.239629 | 35.061805 | 32.295173 |
| Odisha | 9009306.0 | 8989693.0 | 12256258.0 | 32197092.0 | 27.981738 | 27.920823 | 38.066351 |
| Punjab | 9296070.0 | 7904914.0 | 16717805.0 | 36801089.0 | 25.260312 | 21.480109 | 45.427474 |
| Bihar | 8019997.0 | 5984896.0 | 22179969.0 | 37391988.0 | 21.448437 | 16.005825 | 59.317437 |
| Haryana | 8040424.0 | 12561411.0 | 19226568.0 | 43746207.0 | 18.379705 | 28.714286 | 43.950252 |
| Madhya Pradesh | 17854020.0 | 10044889.0 | 18117360.0 | 48198169.0 | 37.042942 | 20.840810 | 37.589312 |
| Telangana | 9133354.0 | 9924001.0 | 28471410.0 | 51117765.0 | 17.867280 | 19.413996 | 55.697682 |
| Kerala | 6489442.0 | 12070040.0 | 29673778.0 | 52600230.0 | 12.337288 | 22.946744 | 56.413780 |
| Andhra Pradesh | 16303716.0 | 10488884.0 | 22032942.0 | 52646842.0 | 30.968080 | 19.923102 | 41.850453 |
| Rajasthan | 19113780.0 | 13028794.0 | 26015812.0 | 61219447.0 | 31.221746 | 21.282116 | 42.495993 |
| Gujarat | 15887187.0 | 33023538.0 | 30220377.0 | 89502727.0 | 17.750506 | 36.896684 | 33.764756 |
| Karnataka | 12066304.0 | 20484404.0 | 50490630.0 | 92178806.0 | 13.090107 | 22.222466 | 54.774663 |
| Uttar Pradesh | 25999255.0 | 25548724.0 | 45968959.0 | 104337115.0 | 24.918511 | 24.486707 | 44.058108 |
| Tamil Nadu | 13329774.0 | 32841892.0 | 53343788.0 | 109256373.0 | 12.200454 | 30.059475 | 48.824418 |
| Maharashtra | 21758383.0 | 47445207.0 | 88631076.0 | 179212165.0 | 12.141131 | 26.474323 | 49.455948 |
plt.figure(figsize=(12,10), dpi =600)
bars1 = pst['primary_percentage']
bars2 = pst['secondary_percentage']
bars3 = pst['tertiary_percentage']
legends = ['Primary %', 'Secondary %', 'Tertiary %']
bars = np.add(bars1, bars2).tolist()
r = np.arange(0,len(pst.index))
names = pst.index
barWidth = 1
# Create red bars
plt.bar(r, bars1, color='red', edgecolor='white')
# Create green bars (middle), on top of the firs ones
plt.bar(r, bars2, bottom=bars1, color='green', edgecolor='white')
# Create blue bars (top)
plt.bar(r, bars3, bottom=bars, color='blue', edgecolor='white')
plt.xticks(r, names,rotation=90)
plt.xlabel('States',fontsize=12)
plt.ylabel('Percentage contribution to GDP',fontsize=12)
plt.title('Percentage contribution of the Primary, Secondary and Tertiary sectors as a percentage of the total GDP for all the states')
plt.legend(legends)
plt.tight_layout()
gdp_per_capita
| GDP per capita | |
|---|---|
| Bihar | 33954 |
| Uttar Pradesh | 49450 |
| Assam | 60621 |
| Jharkhand | 62091 |
| Madhya Pradesh | 62989 |
| Odisha | 73979 |
| Meghalaya | 76228.0 |
| Tripura | 77358.0 |
| Rajasthan | 84837 |
| Chhatisgarh | 86860 |
| Nagaland | 89607 |
| Mizoram | 97687 |
| Andhra Pradesh | 104977 |
| Arunachal Pradesh | 112718 |
| Punjab | 126606 |
| Telangana | 139035 |
| Gujarat | 141263.0 |
| Karnataka | 145141 |
| Tamil Nadu | 146503.0 |
| Himachal Pradesh | 147330 |
| Maharashtra | 152853 |
| Uttrakhand | 153076.0 |
| Kerala | 154778.0 |
| Haryana | 164077.0 |
| Sikkim | 240274 |
| Goa | 271793 |
C1 = gdp_per_capita[gdp_per_capita['GDP per capita'] > gdp_per_capita['GDP per capita'].quantile(0.85)]
C1
| GDP per capita | |
|---|---|
| Kerala | 154778.0 |
| Haryana | 164077.0 |
| Sikkim | 240274 |
| Goa | 271793 |
C2 = gdp_per_capita[(gdp_per_capita['GDP per capita'] > gdp_per_capita['GDP per capita'].quantile(0.50)) & (gdp_per_capita['GDP per capita'] < gdp_per_capita['GDP per capita'].quantile(0.85))]
C2
| GDP per capita | |
|---|---|
| Arunachal Pradesh | 112718 |
| Punjab | 126606 |
| Telangana | 139035 |
| Gujarat | 141263.0 |
| Karnataka | 145141 |
| Tamil Nadu | 146503.0 |
| Himachal Pradesh | 147330 |
| Maharashtra | 152853 |
| Uttrakhand | 153076.0 |
C3 = gdp_per_capita[(gdp_per_capita['GDP per capita'] > gdp_per_capita['GDP per capita'].quantile(0.20)) & (gdp_per_capita['GDP per capita'] <= gdp_per_capita['GDP per capita'].quantile(0.50))]
C3
| GDP per capita | |
|---|---|
| Meghalaya | 76228.0 |
| Tripura | 77358.0 |
| Rajasthan | 84837 |
| Chhatisgarh | 86860 |
| Nagaland | 89607 |
| Mizoram | 97687 |
| Andhra Pradesh | 104977 |
C4 = gdp_per_capita[gdp_per_capita['GDP per capita'] < gdp_per_capita['GDP per capita'].quantile(0.20)]
C4
| GDP per capita | |
|---|---|
| Bihar | 33954 |
| Uttar Pradesh | 49450 |
| Assam | 60621 |
| Jharkhand | 62091 |
| Madhya Pradesh | 62989 |
C1_df = df_final[['S.No.','Item']+list(states for states in C1.index)]
C2_df = df_final[['S.No.','Item']+list(states for states in C2.index)]
C3_df = df_final[['S.No.','Item']+list(states for states in C3.index)]
C4_df = df_final[['S.No.','Item']+list(states for states in C4.index)]
C1_df.reset_index(drop=True, inplace=True)
C2_df.reset_index(drop=True, inplace=True)
C3_df.reset_index(drop=True, inplace=True)
C4_df.reset_index(drop=True, inplace=True)
C1_df
| S.No. | Item | Kerala | Haryana | Sikkim | Goa | |
|---|---|---|---|---|---|---|
| 0 | 1 | Agriculture, forestry and fishing | 5930617.0 | 8015238.0 | 137447 | 308507 |
| 1 | 1.1 | Crops | 3070386.0 | 4636731.0 | 114976 | 140421 |
| 2 | 1.2 | Livestock | 1656104.0 | 2916173.0 | 17338 | 30141 |
| 3 | 1.3 | Forestry and logging | 499808.0 | 352254.0 | 4529 | 15744 |
| 4 | 1.4 | Fishing and aquaculture | 704319.0 | 110080.0 | 604 | 122201 |
| 5 | 2 | Mining and quarrying | 558824.0 | 25186.0 | 1329 | 3622 |
| 6 | Total | Primary | 6489442.0 | 8040424.0 | 138776 | 312129 |
| 7 | 3 | Manufacturing | 4273567.0 | 7756921.0 | 550697 | 1177608 |
| 8 | 4 | Electricity, gas, water supply & other utility... | 482470.0 | 1101919.0 | 212499 | 204110 |
| 9 | 5 | Construction | 7314003.0 | 3702571.0 | 82058 | 165819 |
| 10 | Total | Secondary | 12070040.0 | 12561411.0 | 845253 | 1547536 |
| 11 | 6 | Trade, repair, hotels and restaurants | 8557345.0 | 4986319.0 | 70568 | 380927 |
| 12 | 6.1 | Trade & repair services | NaN | 4817784.0 | 64624 | 343492 |
| 13 | 6.2 | Hotels & restaurants | 793498.0 | 168535.0 | 5945 | 37434 |
| 14 | 7 | Transport, storage, communication & services r... | 4020934.0 | 2560623.0 | 47347 | 189656 |
| 15 | 7.1 | Railways | 147897.0 | 423873.0 | 0 | 15649 |
| 16 | 7.2 | Road transport | NaN | 1452364.0 | 35283 | 46171 |
| 17 | 7.3 | Water transport | 26956.0 | NaN | 0 | 17820 |
| 18 | 7.4 | Air transport | 125029.0 | NaN | 0 | 46359 |
| 19 | 7.5 | Services incidental to transport | 71567.0 | 190269.0 | 0 | 19272 |
| 20 | 7.6 | Storage | 3290.0 | 14459.0 | 0 | 357 |
| 21 | 7.7 | Communication & services related to broadcasting | 884767.0 | 479658.0 | 12064 | 44028 |
| 22 | 8 | Financial services | 2010306.0 | 1671486.0 | 21079 | 233618 |
| 23 | 9 | Real estate, ownership of dwelling & professio... | 7287633.0 | 6970183.0 | 75330 | 407099 |
| 24 | 10 | Public administration | 2068915.0 | 1036377.0 | 119514 | 346486 |
| 25 | 11 | Other services | 5728645.0 | 2001581.0 | 149265 | 180431 |
| 26 | Total | Tertiary | 29673778.0 | 19226568.0 | 483103 | 1738217 |
| 27 | 12 | TOTAL GSVA at basic prices | 48233259.0 | 39828404.0 | 1467133 | 3597882 |
| 28 | 13 | Taxes on Products | 5189352.0 | 4985670.0 | 72200 | 527279 |
| 29 | 14 | Subsidies on products | 822381.0 | 1067867.0 | 18400 | 61854 |
| 30 | 15 | Gross State Domestic Product | 52600230.0 | 43746207.0 | 1520933 | 4063307 |
| 31 | 16 | Population ('00) | 339843.0 | 266620.0 | 6330 | 14950 |
| 32 | 17 | Per Capita GSDP (Rs.) | 154778.0 | 164077.0 | 240274 | 271793 |
C1_df['Total for all states'] = C1_df['Kerala']+C1_df['Haryana']+C1_df['Sikkim']+C1_df['Goa']
C1_df['Percentage of Total GDP'] = C1_df['Total for all states']/C1_df['Total for all states'][11] * 100
C1_df
C:\Users\cheta\AppData\Local\Temp\ipykernel_33516\3086606009.py:1: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy C1_df['Total for all states'] = C1_df['Kerala']+C1_df['Haryana']+C1_df['Sikkim']+C1_df['Goa'] C:\Users\cheta\AppData\Local\Temp\ipykernel_33516\3086606009.py:2: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy C1_df['Percentage of Total GDP'] = C1_df['Total for all states']/C1_df['Total for all states'][11] * 100
| S.No. | Item | Kerala | Haryana | Sikkim | Goa | Total for all states | Percentage of Total GDP | |
|---|---|---|---|---|---|---|---|---|
| 0 | 1 | Agriculture, forestry and fishing | 5930617.0 | 8015238.0 | 137447 | 308507 | 14391809.0 | 102.834194 |
| 1 | 1.1 | Crops | 3070386.0 | 4636731.0 | 114976 | 140421 | 7962514.0 | 56.894773 |
| 2 | 1.2 | Livestock | 1656104.0 | 2916173.0 | 17338 | 30141 | 4619756.0 | 33.009671 |
| 3 | 1.3 | Forestry and logging | 499808.0 | 352254.0 | 4529 | 15744 | 872335.0 | 6.233120 |
| 4 | 1.4 | Fishing and aquaculture | 704319.0 | 110080.0 | 604 | 122201 | 937204.0 | 6.696630 |
| 5 | 2 | Mining and quarrying | 558824.0 | 25186.0 | 1329 | 3622 | 588961.0 | 4.208319 |
| 6 | Total | Primary | 6489442.0 | 8040424.0 | 138776 | 312129 | 14980771.0 | 107.042521 |
| 7 | 3 | Manufacturing | 4273567.0 | 7756921.0 | 550697 | 1177608 | 13758793.0 | 98.311087 |
| 8 | 4 | Electricity, gas, water supply & other utility... | 482470.0 | 1101919.0 | 212499 | 204110 | 2000998.0 | 14.297787 |
| 9 | 5 | Construction | 7314003.0 | 3702571.0 | 82058 | 165819 | 11264451.0 | 80.488196 |
| 10 | Total | Secondary | 12070040.0 | 12561411.0 | 845253 | 1547536 | 27024240.0 | 193.097056 |
| 11 | 6 | Trade, repair, hotels and restaurants | 8557345.0 | 4986319.0 | 70568 | 380927 | 13995159.0 | 100.000000 |
| 12 | 6.1 | Trade & repair services | NaN | 4817784.0 | 64624 | 343492 | NaN | NaN |
| 13 | 6.2 | Hotels & restaurants | 793498.0 | 168535.0 | 5945 | 37434 | 1005412.0 | 7.183998 |
| 14 | 7 | Transport, storage, communication & services r... | 4020934.0 | 2560623.0 | 47347 | 189656 | 6818560.0 | 48.720847 |
| 15 | 7.1 | Railways | 147897.0 | 423873.0 | 0 | 15649 | 587419.0 | 4.197301 |
| 16 | 7.2 | Road transport | NaN | 1452364.0 | 35283 | 46171 | NaN | NaN |
| 17 | 7.3 | Water transport | 26956.0 | NaN | 0 | 17820 | NaN | NaN |
| 18 | 7.4 | Air transport | 125029.0 | NaN | 0 | 46359 | NaN | NaN |
| 19 | 7.5 | Services incidental to transport | 71567.0 | 190269.0 | 0 | 19272 | 281108.0 | 2.008609 |
| 20 | 7.6 | Storage | 3290.0 | 14459.0 | 0 | 357 | 18106.0 | 0.129373 |
| 21 | 7.7 | Communication & services related to broadcasting | 884767.0 | 479658.0 | 12064 | 44028 | 1420517.0 | 10.150060 |
| 22 | 8 | Financial services | 2010306.0 | 1671486.0 | 21079 | 233618 | 3936489.0 | 28.127505 |
| 23 | 9 | Real estate, ownership of dwelling & professio... | 7287633.0 | 6970183.0 | 75330 | 407099 | 14740245.0 | 105.323884 |
| 24 | 10 | Public administration | 2068915.0 | 1036377.0 | 119514 | 346486 | 3571292.0 | 25.518052 |
| 25 | 11 | Other services | 5728645.0 | 2001581.0 | 149265 | 180431 | 8059922.0 | 57.590785 |
| 26 | Total | Tertiary | 29673778.0 | 19226568.0 | 483103 | 1738217 | 51121666.0 | 365.281066 |
| 27 | 12 | TOTAL GSVA at basic prices | 48233259.0 | 39828404.0 | 1467133 | 3597882 | 93126678.0 | 665.420650 |
| 28 | 13 | Taxes on Products | 5189352.0 | 4985670.0 | 72200 | 527279 | 10774501.0 | 76.987343 |
| 29 | 14 | Subsidies on products | 822381.0 | 1067867.0 | 18400 | 61854 | 1970502.0 | 14.079883 |
| 30 | 15 | Gross State Domestic Product | 52600230.0 | 43746207.0 | 1520933 | 4063307 | 101930677.0 | 728.328110 |
| 31 | 16 | Population ('00) | 339843.0 | 266620.0 | 6330 | 14950 | 627743.0 | 4.485430 |
| 32 | 17 | Per Capita GSDP (Rs.) | 154778.0 | 164077.0 | 240274 | 271793 | 830922.0 | 5.937210 |
C1_contributor = C1_df[['Item','Percentage of Total GDP']][:-2].sort_values(by='Percentage of Total GDP', ascending=False)
C1_contributor.reset_index(drop=True, inplace=True)
C1_contributor['Cumulative sum'] = C1_contributor['Percentage of Total GDP'].cumsum()
C1_contributor
| Item | Percentage of Total GDP | Cumulative sum | |
|---|---|---|---|
| 0 | Gross State Domestic Product | 728.328110 | 728.328110 |
| 1 | TOTAL GSVA at basic prices | 665.420650 | 1393.748760 |
| 2 | Tertiary | 365.281066 | 1759.029826 |
| 3 | Secondary | 193.097056 | 1952.126882 |
| 4 | Primary | 107.042521 | 2059.169403 |
| 5 | Real estate, ownership of dwelling & professio... | 105.323884 | 2164.493287 |
| 6 | Agriculture, forestry and fishing | 102.834194 | 2267.327481 |
| 7 | Trade, repair, hotels and restaurants | 100.000000 | 2367.327481 |
| 8 | Manufacturing | 98.311087 | 2465.638568 |
| 9 | Construction | 80.488196 | 2546.126764 |
| 10 | Taxes on Products | 76.987343 | 2623.114107 |
| 11 | Other services | 57.590785 | 2680.704892 |
| 12 | Crops | 56.894773 | 2737.599666 |
| 13 | Transport, storage, communication & services r... | 48.720847 | 2786.320513 |
| 14 | Livestock | 33.009671 | 2819.330184 |
| 15 | Financial services | 28.127505 | 2847.457689 |
| 16 | Public administration | 25.518052 | 2872.975741 |
| 17 | Electricity, gas, water supply & other utility... | 14.297787 | 2887.273528 |
| 18 | Subsidies on products | 14.079883 | 2901.353411 |
| 19 | Communication & services related to broadcasting | 10.150060 | 2911.503471 |
| 20 | Hotels & restaurants | 7.183998 | 2918.687469 |
| 21 | Fishing and aquaculture | 6.696630 | 2925.384099 |
| 22 | Forestry and logging | 6.233120 | 2931.617218 |
| 23 | Mining and quarrying | 4.208319 | 2935.825538 |
| 24 | Railways | 4.197301 | 2940.022839 |
| 25 | Services incidental to transport | 2.008609 | 2942.031448 |
| 26 | Storage | 0.129373 | 2942.160821 |
| 27 | Trade & repair services | NaN | NaN |
| 28 | Road transport | NaN | NaN |
| 29 | Water transport | NaN | NaN |
| 30 | Air transport | NaN | NaN |
plt.figure(figsize=(6,4), dpi=600)
sns.barplot(y=C1_contributor['Item'], x = C1_contributor['Percentage of Total GDP'], palette='inferno')
plt.xlabel("Percentage of Total GSDP for C1 States")
plt.ylabel('Sub-sectors')
plt.title('Percentage of Total GSDP for C1 States vs Sub-sectors')
plt.savefig("Percentage of Total GSDP for C1 States vs Sub-sectors.png", bbox_inches='tight', dpi=600)
plt.show()
C2_df['Total for all states']=list(C2_df[list(states for states in C2_df.columns)[2:]].sum(axis=1))
C2_df['Percentage of Total GDP'] = C2_df['Total for all states']/C2_df['Total for all states'][11] * 100
C2_contributor = C2_df[['Item','Percentage of Total GDP']][:-2].sort_values(by='Percentage of Total GDP', ascending=False)
C2_contributor.reset_index(drop=True, inplace=True)
C2_contributor['Cumulative sum'] = C2_contributor['Percentage of Total GDP'].cumsum()
C2_contributor
C:\Users\cheta\AppData\Local\Temp\ipykernel_33516\3546390381.py:1: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy C2_df['Total for all states']=list(C2_df[list(states for states in C2_df.columns)[2:]].sum(axis=1)) C:\Users\cheta\AppData\Local\Temp\ipykernel_33516\3546390381.py:2: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy C2_df['Percentage of Total GDP'] = C2_df['Total for all states']/C2_df['Total for all states'][11] * 100
| Item | Percentage of Total GDP | Cumulative sum | |
|---|---|---|---|
| 0 | Gross State Domestic Product | 957.529962 | 957.529962 |
| 1 | TOTAL GSVA at basic prices | 861.355113 | 1818.885075 |
| 2 | Tertiary | 454.332791 | 2273.217866 |
| 3 | Secondary | 267.270597 | 2540.488463 |
| 4 | Manufacturing | 178.312474 | 2718.800937 |
| 5 | Real estate, ownership of dwelling & professio... | 150.429716 | 2869.230653 |
| 6 | Primary | 139.751730 | 3008.982383 |
| 7 | Agriculture, forestry and fishing | 122.812573 | 3131.794956 |
| 8 | Taxes on Products | 117.412440 | 3249.207396 |
| 9 | Trade, repair, hotels and restaurants | 100.000000 | 3349.207396 |
| 10 | Trade & repair services | 90.224299 | 3439.431695 |
| 11 | Crops | 77.646928 | 3517.078622 |
| 12 | Construction | 66.385234 | 3583.463857 |
| 13 | Financial services | 59.270602 | 3642.734458 |
| 14 | Other services | 59.200409 | 3701.934868 |
| 15 | Transport, storage, communication & services r... | 55.111749 | 3757.046617 |
| 16 | Livestock | 31.620266 | 3788.666883 |
| 17 | Public administration | 30.320314 | 3818.987196 |
| 18 | Road transport | 24.247676 | 3843.234873 |
| 19 | Electricity, gas, water supply & other utility... | 22.572885 | 3865.807758 |
| 20 | Subsidies on products | 21.237591 | 3887.045349 |
| 21 | Mining and quarrying | 16.939155 | 3903.984504 |
| 22 | Communication & services related to broadcasting | 15.364200 | 3919.348704 |
| 23 | Hotels & restaurants | 9.775698 | 3929.124401 |
| 24 | Forestry and logging | 9.149767 | 3938.274168 |
| 25 | Fishing and aquaculture | 4.395611 | 3942.669779 |
| 26 | Railways | 4.073423 | 3946.743202 |
| 27 | Services incidental to transport | 2.833433 | 3949.576635 |
| 28 | Air transport | 1.077487 | 3950.654122 |
| 29 | Storage | 0.601767 | 3951.255889 |
| 30 | Water transport | 0.519592 | 3951.775480 |
plt.figure(figsize=(6,4), dpi=600)
sns.barplot(y=C2_contributor['Item'], x = C2_contributor['Percentage of Total GDP'],palette='hot')
plt.xlabel("Percentage of Total GSDP for C2 States")
plt.ylabel('Sub-sectors')
plt.title('Percentage of Total GSDP for C2 States vs Sub-sectors')
plt.show()
C3_df['Total for all states']=list(C3_df[list(states for states in C3_df.columns)[2:]].sum(axis=1))
C3_df['Percentage of Total GDP'] = C3_df['Total for all states']/C3_df['Total for all states'][11] * 100
C3_contributor = C3_df[['Item','Percentage of Total GDP']][:-2].sort_values(by='Percentage of Total GDP', ascending=False)
C3_contributor.reset_index(drop=True, inplace=True)
C3_contributor['Cumulative sum'] = C3_contributor['Percentage of Total GDP'].cumsum()
C3_contributor
C:\Users\cheta\AppData\Local\Temp\ipykernel_33516\3224460790.py:1: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy C3_df['Total for all states']=list(C3_df[list(states for states in C3_df.columns)[2:]].sum(axis=1)) C:\Users\cheta\AppData\Local\Temp\ipykernel_33516\3224460790.py:2: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy C3_df['Percentage of Total GDP'] = C3_df['Total for all states']/C3_df['Total for all states'][11] * 100
| Item | Percentage of Total GDP | Cumulative sum | |
|---|---|---|---|
| 0 | Gross State Domestic Product | 1034.136202 | 1034.136202 |
| 1 | TOTAL GSVA at basic prices | 974.535729 | 2008.671931 |
| 2 | Tertiary | 425.330004 | 2434.001934 |
| 3 | Primary | 312.529255 | 2746.531189 |
| 4 | Agriculture, forestry and fishing | 254.004929 | 3000.536119 |
| 5 | Secondary | 236.676463 | 3237.212582 |
| 6 | Crops | 136.875835 | 3374.088417 |
| 7 | Manufacturing | 115.524874 | 3489.613291 |
| 8 | Trade, repair, hotels and restaurants | 100.000000 | 3589.613291 |
| 9 | Taxes on Products | 99.120409 | 3688.733700 |
| 10 | Real estate, ownership of dwelling & professio... | 98.436763 | 3787.170463 |
| 11 | Construction | 94.393851 | 3881.564314 |
| 12 | Trade & repair services | 92.698553 | 3974.262867 |
| 13 | Other services | 74.311839 | 4048.574706 |
| 14 | Livestock | 73.408993 | 4121.983698 |
| 15 | Transport, storage, communication & services r... | 72.344253 | 4194.327952 |
| 16 | Mining and quarrying | 58.524312 | 4252.852263 |
| 17 | Public administration | 46.886119 | 4299.738382 |
| 18 | Subsidies on products | 39.519936 | 4339.258318 |
| 19 | Road transport | 39.010677 | 4378.268995 |
| 20 | Financial services | 33.351029 | 4411.620024 |
| 21 | Electricity, gas, water supply & other utility... | 26.757753 | 4438.377777 |
| 22 | Forestry and logging | 23.088031 | 4461.465807 |
| 23 | Fishing and aquaculture | 20.632064 | 4482.097871 |
| 24 | Communication & services related to broadcasting | 17.996022 | 4500.093894 |
| 25 | Railways | 7.439935 | 4507.533829 |
| 26 | Hotels & restaurants | 7.301454 | 4514.835283 |
| 27 | Services incidental to transport | 5.909872 | 4520.745155 |
| 28 | Water transport | 0.674891 | 4521.420046 |
| 29 | Storage | 0.373999 | 4521.794045 |
| 30 | Air transport | 0.309427 | 4522.103472 |
plt.figure(figsize=(6,4), dpi=600)
sns.barplot(y=C3_contributor['Item'], x = C3_contributor['Percentage of Total GDP'], palette='autumn')
plt.xlabel("Percentage of Total GSDP for C3 States")
plt.ylabel('Sub-sectors')
plt.title('Percentage of Total GSDP for C3 States vs Sub-sectors')
plt.show()
C4_df['Total for all states']=list(C4_df[list(states for states in C4_df.columns)[2:]].sum(axis=1))
C4_df['Percentage of Total GDP'] = C4_df['Total for all states']/C4_df['Total for all states'][11] * 100
C4_contributor = C4_df[['Item','Percentage of Total GDP']][:-2].sort_values(by='Percentage of Total GDP', ascending=False)
C4_contributor.reset_index(drop=True, inplace=True)
C4_contributor['Cumulative sum'] = C4_contributor['Percentage of Total GDP'].cumsum()
C4_contributor
C:\Users\cheta\AppData\Local\Temp\ipykernel_33516\791793354.py:1: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy C4_df['Total for all states']=list(C4_df[list(states for states in C4_df.columns)[2:]].sum(axis=1)) C:\Users\cheta\AppData\Local\Temp\ipykernel_33516\791793354.py:2: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy C4_df['Percentage of Total GDP'] = C4_df['Total for all states']/C4_df['Total for all states'][11] * 100
| Item | Percentage of Total GDP | Cumulative sum | |
|---|---|---|---|
| 0 | Gross State Domestic Product | 850.234400 | 850.234400 |
| 1 | TOTAL GSVA at basic prices | 800.863184 | 1651.097584 |
| 2 | Tertiary | 380.971605 | 2032.069190 |
| 3 | Primary | 229.406867 | 2261.476057 |
| 4 | Agriculture, forestry and fishing | 207.011384 | 2468.487441 |
| 5 | Secondary | 190.484708 | 2658.972149 |
| 6 | Crops | 143.652127 | 2802.624276 |
| 7 | Trade, repair, hotels and restaurants | 100.000000 | 2902.624276 |
| 8 | Trade & repair services | 93.042143 | 2995.666419 |
| 9 | Manufacturing | 91.609199 | 3087.275618 |
| 10 | Real estate, ownership of dwelling & professio... | 88.231990 | 3175.507608 |
| 11 | Construction | 83.142536 | 3258.650144 |
| 12 | Taxes on Products | 80.193985 | 3338.844129 |
| 13 | Transport, storage, communication & services r... | 59.078259 | 3397.922389 |
| 14 | Other services | 57.090911 | 3455.013299 |
| 15 | Public administration | 48.452443 | 3503.465742 |
| 16 | Livestock | 43.443084 | 3546.908826 |
| 17 | Subsidies on products | 30.822770 | 3577.731596 |
| 18 | Road transport | 28.450103 | 3606.181698 |
| 19 | Financial services | 28.118003 | 3634.299702 |
| 20 | Mining and quarrying | 22.395483 | 3656.695185 |
| 21 | Communication & services related to broadcasting | 16.238468 | 3672.933653 |
| 22 | Electricity, gas, water supply & other utility... | 15.732973 | 3688.666625 |
| 23 | Forestry and logging | 13.326865 | 3701.993491 |
| 24 | Railways | 12.141441 | 3714.134932 |
| 25 | Hotels & restaurants | 6.957857 | 3721.092788 |
| 26 | Fishing and aquaculture | 6.589304 | 3727.682093 |
| 27 | Services incidental to transport | 1.002114 | 3728.684207 |
| 28 | Storage | 0.815307 | 3729.499514 |
| 29 | Air transport | 0.403598 | 3729.903112 |
| 30 | Water transport | 0.027225 | 3729.930337 |
plt.figure(figsize=(6,4), dpi=600)
sns.barplot(y=C4_contributor['Item'], x = C4_contributor['Percentage of Total GDP'], palette='spring')
plt.xlabel("Percentage of Total GSDP for C4 States")
plt.ylabel('Sub-sectors')
plt.title('Percentage of Total GSDP for C4 States vs Sub-sectors')
plt.show()